Context-aware geometric deep learning for RNA sequence design

Parth Bibekar, Lucien F. Krapp, Matteo Dal Peraro

bioRxiv, 2025

RNA design has emerged to play a crucial role in synthetic biology and therapeutics. We present RISoTTo, a parameter-free geometric deep learning approach that generates RNA sequences conditioned on both their backbone scaffolds and the surrounding molecular context.

PeSTo-Carbs: Geometric Deep Learning for Prediction of Protein-Carbohydrate Binding Interfaces

Parth Bibekar, Lucien F. Krapp, Matteo Dal Peraro

Journal of Chemical Theory and Computation, 2024

The Protein Structure Transformer (PeSTo) has exhibited exceptional performance in predicting protein-protein binding interfaces. We introduce PeSTo-Carbs, an extension specifically engineered to predict protein-carbohydrate binding interfaces, including cyclodextrins, with remarkable accuracy despite scarce structural data.

Conformational ensemble of the NSP1 CTD in SARS-CoV-2: Perspectives from the free energy landscape

Pallab Dutta, Abhay Kshirsagar, Parth Bibekar, Neelanjana Sengupta

Biophysical Journal, 2023

We investigate the conformational ensemble of NSP1 CTD to reveal the thermokinetics in its order-disorder transitions. Our analyses determine the existence of two distinct unfolded populations separated by high kinetic barriers from the predominant one that is compatible with complex formation with the ribosome.